There has been extensive research examining the influence of race and socioeconomic status (SES) on disparities in cancer survival. The problem with most studies that model this question is that (1) race and SES are closely entwined, so coefficients on race and income are difficult to interpret; (2) model coefficients on covariates ar dominated by the majority population; and (3) the determinants of disparities are not transparent using standard regression models since formal testing of race coefficient differences across models lacks clear interpretation when models are composed of different covariates. Recently we introduced the concept of Tapered Multivariate Matching (TMM) and applied this methodology to the study of racial disparities in breast cancer treatment and survival. By using a focal group (the minority population) and a series of tapered matches to the focal group, forcing the majority population to more closely resemble the minority population in sequential matches, the method allows one to (1) observe and formally compare majority to minority patient covariates, treatments, and outcomes inside and across each match and (2) observe and formally compare the changing majority population characteristics across each match, thereby uncovering potential causes for observed disparities. In the present study we propose to reorient and update the TMM study design specifically to examine SES across and within racial strata. The overall goal of the study is to better understand why breast and colon cancer patients with lower SES in the Medicare system have worse survival and to suggest potential remedies to improve these disparities. This is a retrospective observational study that utilizes secondary data in the SEER-Medicare database for over 250,000 elderly breast and colon cancer patients diagnosed between 1991 and 2009. The study has three aims. AIM 1: To determine the overall SES-associated disparity in presentation and treatment factors and survival between patients defined as having lower income and education through a number of alternative definitions of SES using patient level information (Medicare Dual Eligibility) and neighborhood level information on income, education, and poverty (from the patient's neighborhood level data), accounting for race. AIM 2: To determine whether disparities in presentation factors, treatment, and survival have changed over time (over the last two decades). AIM 3: To understand why survival disparities exist through use of Tapered Multivariate Matching. This study builds on our previous work but requires a modified matching design and completely new matches. Results from this study should aid in understanding why disparities in cancer survival persist by race and SES and should help in reducing such disparities in the future.